Then, we employ a comprehensive user study to measure the gap between the network and real oncologists. Following the general workflow of deep learning-assisted organs delineation systems [1]}, we invite three junior oncologists ( with 1-3 years experience) from three different hospitals to revise model-generated predictions independently until the results are clinical acceptable. We randomly selected 20 predictions produced by nnUNetV2 (3D) for the user study and calculate the revised results. The quantitative comparison in terms of \(DSC\)  between the nnUNet predictions and three oncologists' revised results are presented in Fig. REF . It can be found that for large size and clear boundary organs the deep network can produce promising results that are very close to clinically applicable just with few revisions. However, there is a huge gap between the deep network and junior oncologists in small organs segmentation. It indicates that the deep network has the potential to reduce the burden of oncologists in the large size of organs delineation. In the future, combining the user interaction with the deep network may help to further reduce the small size of organs delineation burden and accelerate the clinical workflow [2]}, [3]}.
